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Developing a ROMI Analysis Introduction and Discussion
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Methodology Overview Build database Normalize data from various sources Build parallel models to look at different variables and variable combinations Refine models (focus on DMAs with enough advertising data to make confident conclusions) Generate and test hypotheses with models Find themes that emerge from the models Translate mathematical results into actionable business recommendations Drill down to gain better understanding of relationships
Why is the OG ROMI approach different?  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],“ X in combination with Y”
Methodology ,[object Object],GMAX –  Genetic Approach - 10s of thousands of model combinations - Determines important variables Regression - Seeks to understand and calibrate individual variable influence
[object Object],$10 $9 $5 $4 $7 $8 $3 $2 $1 $6 R R R N R N N N R
[object Object],$10 $9 $5 $4 $7 $8 $3 $2 $1 $6 R R R N R N N N N R
GMAX ™ Benefits ,[object Object],[object Object],[object Object],[object Object]
Case example   HP:  Small Business Target
Project Objective ,[object Object],Total Sales $ Client Controlled  Attitudinal Outcomes Sales Outcomes
Print Costs ,[object Object]
Marketing Communications  Variable Tree Share of voice, print, online, and direct mail  all  have an affect on sales Note how Print has an impact by itself  AND in combination with Direct Mail Sales Shipments Prod B Share of voice Prod A Share of voice Print Out of pocket  Direct Mail Print Out of pocket  Online costs
Catalog Circulation ,[object Object],[object Object],[object Object]
Other variables examined:  Supply Chain  An example from one set of modeling … Variable Relative Frequency AvFqcy  Variable 1.83000  PRTTOOP   Print out of pocket costs 1.07000  WTPcompAP Wtd average pricing vs Comp A 0.75000  Category SOV Share of voice 0.53500  BRANDSOV  Share of voice 0.31000  CATTOOP Catalog out of pocket costs 0.15500  PRTCIRC Print circulation  0.08500  SOVSOM Share of voice / Share of market ratio 0.06500  WTD compaPR Wtd average proposed pricing vs. CompA 0.06500  SERSPEND Total spending on XXXX 0.04500  BACKDOL $ value of   backlog 0.04500  CATQTY Catalog circulation (quantity) 0.03000  SPCompBPR Proposed price vs CompB 0.02500  DIRMAIL Direct mail circulation 0.01000  SPRICE Price vs CompS 0.00500  DMCOST Direct mail total out of pocket costs 0.00500  BACKQTY Backlog quantity (units)
ROMI Model This analysis yields a moderately complicated, but understandable and interpretable model Total Sales $  =  240,000,000 + 1.984 *  ((Print Out of Pocket $/Print Circulation) *   (Catalog Out of Pocket $) * (Weighted Pricing vs. compA/Weighted Proposed Pricing vs compA)) + $335 * (Direct Mail Cost)
Sales per $  ,[object Object]
Case example   U.S. Navy:  Recruiting
Lead    Contract Lag Although the lag between a lead and a contract varies, almost 60% of contracts are signed within 4 months of generating a lead Cume 58.2%
Positive Impact on Leads Total Spend General Mkt Spend TV Spend Radio Spend Internet Spend Internet CPL Each of these variables contribute to lead generation at > 85% CL
Influence on Leads per $ Spend Of the variables that have a positive correlation with leads, Internet (specifically CPL), Direct Marketing, Radio and TV have the highest rate of return per an additional $100K of spending
Positive Impact on Contracts Total Spend TV Spend Media Events Direct Marketing Internet CPL Each of these variables contribute to contracts at > 85% CL
Contracts Total media spend Print Media Events War Handling Media Events Media Events  work by themselves, but also act as  catalysts  to  Print  and  Direct Marketing
Contracts: Influence per $ Spend Of the variables that have positive correlation with number of contracts, Internet (specifically CPL and Search), Direct Marketing and Media Events have the highest rate of return per additional $100K spent
Implication:  Media Influences LEADS CONTRACTS Overall Spending General Market Spend Radio TV Internet CPL Direct Marketing Media Events
Discussion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
In Summary… ,[object Object],[object Object],[object Object],[object Object],[object Object]
Intersecting marketing, science and technology™

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Marketing return On Investment Modelinig

  • 1. Developing a ROMI Analysis Introduction and Discussion
  • 2.
  • 3. Methodology Overview Build database Normalize data from various sources Build parallel models to look at different variables and variable combinations Refine models (focus on DMAs with enough advertising data to make confident conclusions) Generate and test hypotheses with models Find themes that emerge from the models Translate mathematical results into actionable business recommendations Drill down to gain better understanding of relationships
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9. Case example HP: Small Business Target
  • 10.
  • 11.
  • 12. Marketing Communications Variable Tree Share of voice, print, online, and direct mail all have an affect on sales Note how Print has an impact by itself AND in combination with Direct Mail Sales Shipments Prod B Share of voice Prod A Share of voice Print Out of pocket Direct Mail Print Out of pocket Online costs
  • 13.
  • 14. Other variables examined: Supply Chain An example from one set of modeling … Variable Relative Frequency AvFqcy Variable 1.83000 PRTTOOP Print out of pocket costs 1.07000 WTPcompAP Wtd average pricing vs Comp A 0.75000 Category SOV Share of voice 0.53500 BRANDSOV Share of voice 0.31000 CATTOOP Catalog out of pocket costs 0.15500 PRTCIRC Print circulation 0.08500 SOVSOM Share of voice / Share of market ratio 0.06500 WTD compaPR Wtd average proposed pricing vs. CompA 0.06500 SERSPEND Total spending on XXXX 0.04500 BACKDOL $ value of backlog 0.04500 CATQTY Catalog circulation (quantity) 0.03000 SPCompBPR Proposed price vs CompB 0.02500 DIRMAIL Direct mail circulation 0.01000 SPRICE Price vs CompS 0.00500 DMCOST Direct mail total out of pocket costs 0.00500 BACKQTY Backlog quantity (units)
  • 15. ROMI Model This analysis yields a moderately complicated, but understandable and interpretable model Total Sales $ = 240,000,000 + 1.984 * ((Print Out of Pocket $/Print Circulation) * (Catalog Out of Pocket $) * (Weighted Pricing vs. compA/Weighted Proposed Pricing vs compA)) + $335 * (Direct Mail Cost)
  • 16.
  • 17. Case example U.S. Navy: Recruiting
  • 18. Lead  Contract Lag Although the lag between a lead and a contract varies, almost 60% of contracts are signed within 4 months of generating a lead Cume 58.2%
  • 19. Positive Impact on Leads Total Spend General Mkt Spend TV Spend Radio Spend Internet Spend Internet CPL Each of these variables contribute to lead generation at > 85% CL
  • 20. Influence on Leads per $ Spend Of the variables that have a positive correlation with leads, Internet (specifically CPL), Direct Marketing, Radio and TV have the highest rate of return per an additional $100K of spending
  • 21. Positive Impact on Contracts Total Spend TV Spend Media Events Direct Marketing Internet CPL Each of these variables contribute to contracts at > 85% CL
  • 22. Contracts Total media spend Print Media Events War Handling Media Events Media Events work by themselves, but also act as catalysts to Print and Direct Marketing
  • 23. Contracts: Influence per $ Spend Of the variables that have positive correlation with number of contracts, Internet (specifically CPL and Search), Direct Marketing and Media Events have the highest rate of return per additional $100K spent
  • 24. Implication: Media Influences LEADS CONTRACTS Overall Spending General Market Spend Radio TV Internet CPL Direct Marketing Media Events
  • 25.
  • 26.
  • 27. Intersecting marketing, science and technology™

Editor's Notes

  1. 1
  2. The first step in the model building process is collecting all of the various data inputs. At HP, we have a ton of data – but it’s not always easy to get your arms around it. We spent several weeks identifying the data that we wanted to include in the modeling process, finding the owner of the data, and getting data feeds to OG so that they could work it into a form that would be useful in a large-scale modeling process. Once the data was gathered, a data mining tool called GMAX was used to identify the important variables and uncover some unique relationships between the variables … these “driver variables” were then exported to more traditional stat analysis packages to refine and calibrate the end models.
  3. A quick explanation of how GMAX works – Most statistical analysis utilizes regression as the principal way to identify positive (above the line) and negative (below the line) data variables. This is generally displayed as a sloped line through a data plot.
  4. The Optimization Group utilizes a data mining and modeling process called “Genetic Programming” (GP). This is software that literally programs itself, based on the data it is testing. OG’s proprietary GP software product is called “GMAX”.